ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations

• URL: http://arxiv.org/abs/2206.11900v1
• Date: Thu, 23 Jun 2022 08:37:32 GMT
• Title: ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations
• Authors: Ryma Boumazouza (CRIL), Fahima Cheikh-Alili (CRIL), Bertrand Mazure (CRIL), Karim Tabia (CRIL)
• Abstract summary: This paper proposes a generic approach named ASTERYX allowing to generate both symbolic explanations and score-based ones. Our experimental results show the feasibility of the proposed approach and its effectiveness in providing symbolic and score-based explanations.
• Score: 26.500149465292246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever increasing complexity of machine learning techniques used more and more in practice, gives rise to the need to explain the predictions and decisions of these models, often used as black-boxes. Explainable AI approaches are either numerical feature-based aiming to quantify the contribution of each feature in a prediction or symbolic providing certain forms of symbolic explanations such as counterfactuals. This paper proposes a generic agnostic approach named ASTERYX allowing to generate both symbolic explanations and score-based ones. Our approach is declarative and it is based on the encoding of the model to be explained in an equivalent symbolic representation, this latter serves to generate in particular two types of symbolic explanations which are sufficient reasons and counterfactuals. We then associate scores reflecting the relevance of the explanations and the features w.r.t to some properties. Our experimental results show the feasibility of the proposed approach and its effectiveness in providing symbolic and score-based explanations.

Related papers

• The Foundations of Tokenization: Statistical and Computational Concerns [51.370165245628975]
  Tokenization is a critical step in the NLP pipeline. Despite its recognized importance as a standard representation method in NLP, the theoretical underpinnings of tokenization are not yet fully understood. The present paper contributes to addressing this theoretical gap by proposing a unified formal framework for representing and analyzing tokenizer models.
  arXiv  Detail & Related papers  (2024-07-16T11:12:28Z)
• A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data [77.34726150561087]
  This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. The model is able to produce fairly rich yet human-readable conceptual representations without training.
  arXiv  Detail & Related papers  (2023-07-16T15:59:13Z)
• Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces [14.70409833767752]
  Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. We propose two new analyses, extending principles found in PCA or ICA to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), maximize relevance instead of e.g. variance or kurtosis.
  arXiv  Detail & Related papers  (2022-12-30T18:04:25Z)
• A Model-Agnostic SAT-based Approach for Symbolic Explanation Enumeration [26.500149465292246]
  We generate explanations to locally explain a single prediction by analyzing the relationship between the features and the output. Our approach uses a propositional encoding of the predictive model and a SAT-based setting to generate two types of symbolic explanations Sufficient Reasons and Counterfactuals.
  arXiv  Detail & Related papers  (2022-06-23T08:35:47Z)
• A Symbolic Approach for Counterfactual Explanations [18.771531343438227]
  We propose a novel symbolic approach to provide counterfactual explanations for a classifier predictions. Our approach is symbolic in the sense that it is based on encoding the decision function of a classifier in an equivalent CNF formula.
  arXiv  Detail & Related papers  (2022-06-20T08:38:54Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals [26.641834518599303]
  We propose a methodology to evaluate explanations for machine reading comprehension tasks. An explanation should allow us to understand the RC model's high-level behavior with respect to a set of realistic counterfactual input scenarios. Our analysis suggests that pairwise explanation techniques are better suited to RC than token-level attributions.
  arXiv  Detail & Related papers  (2021-04-09T17:55:21Z)
• Contrastive Explanations for Model Interpretability [77.92370750072831]
  We propose a methodology to produce contrastive explanations for classification models. Our method is based on projecting model representation to a latent space. Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
  arXiv  Detail & Related papers  (2021-03-02T00:36:45Z)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)
• Interpretable Representations in Explainable AI: From Theory to Practice [7.031336702345381]
  Interpretable representations are the backbone of many explainers that target black-box predictive systems. We study properties of interpretable representations that encode presence and absence of human-comprehensible concepts.
  arXiv  Detail & Related papers  (2020-08-16T21:44:03Z)
• Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
  We establish a novel set of evaluation criteria for such feature based explanations by analysis. We obtain new explanations that are loosely necessary and sufficient for a prediction. We extend the explanation to extract the set of features that would move the current prediction to a target class.
  arXiv  Detail & Related papers  (2020-05-31T05:52:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.